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Environmental Mapping Based on Spatial Variability

Nelley Kovalevskaya* and Vladimir Pavlov

Institute for Water and Environmental Problems, SB RAS 105 Papanintsev St., 656099 Barnaul, Russia



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Fig. 1. Markov chains of C maps: (a) Single chain of C maps: C = 6; (b) S subchains of CS = C/S maps: S = 3.

 


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Fig. 2. Superposed lattices of image (RIM) and map (RMP): K1, K2, second-order clique families.

 


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Fig. 3. Pairs "map MP–noisy image IM" simulated for the given parameters: (a) ({lambda}1,{lambda}2) = (0.35, 0.40); (b) ({lambda}1,{lambda}2) = (0.17, 0.40); (c) ({lambda}1,{lambda}2) = (0.35, 1.20); (d) ({lambda}1,{lambda}2) = (0.17, 1.20) at the different iterations (It): (a), (b) It = 30; (c), (d) It = 25.

 


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Fig. 4. Segmentation maps of the grayscale image with different estimates of marginals and different number of subchains (S): pair "image IM–map MP" simulated for ({lambda}1,{lambda}2) = (0.17, 1.20): (a) grayscale image, (b) true map; estimates based on sample frequencies: (c) S = 1, (d) S = 4, (e) S = 9; estimates based on averaging of transition probabilities: (f) S = 1, (g) S = 4; (h) S = 9.

 


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Fig. 5. Image of Lake Baikal (eastern Siberia): (a) 0.72/-1.2 µm; (b) 3.55/-3.93 µm; (c) 10.3/-11.3 µm; (d) ISODATA clustering map; (e) segmentation map from Eq. [2] and [4], shoals and dense flows with different concentrations of suspended matter (gray), pure water (light gray), dry land (dark gray); (f) georeferenced map of segmentation shown in (e) by Eq. [2] and [4].

 


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Fig. 6. Different textured types of natural patterns (western Siberia, Altai Mountains): (a, c) learning and (b, d) generated samples.

 


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Fig. 7. Learning sample (LS) and the simulated images after iterations (It).

 


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Fig. 8. Image of Lake Teletskoye in the Altai Mountains (western Siberia): (a) near-infrared (NIR) band; (b) segmentation map by Eq. [2], [4], and [5], shoals and water with sediment load and organic matter (gray), pure water (light gray), mountains (dark gray); (c) georeferenced map of segmentation shown in (b) by Eq. [2], [4], and [5].

 





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